Active learning and domain adaptation are both important tools for reducing labeling effort to learn a good supervised model in a target domain. In this paper, we inves- tigate the problem of online active learn- ing within a new active domain adapta- tion setting: there are insufficient labeled data in both source and target domains, but it is cheaper to query labels in the source domain than in the target domain. Given a total budget, we develop two cost- sensitive online active learning methods, a multi-view uncertainty-based method and a multi-view disagreement-based method, to query the most informative instances from the two domains, aiming to learn a good prediction model in the target do- main. Empirical studies on the tasks of cross-domain sentiment classification of Amazon produc...